Reducing environmental impact of production during a rolling blackout policy – a multi-objective schedule optimisation approach
2015-05-07T10:12:51Z (GMT) by
Many manufacturing companies in China currently are suffering from a Rolling Blackout policy for the industry electricity supply which means that the government electricity is cut off several days in every week resulting in manufacturing companies illegally starting their own diesel generators to maintain production. However, the private generation of electricity is more polluting and costly than the government supplied resource. Thus, the increased price of energy and the requirement to become more environmentally sustainable exert substantial pressures on manufacturing enterprises to reduce energy consumption for cost saving and to become more environmentally friendly. Scheduling of less energy consumption critical operations during rolling blackout periods can help minimise the negative effect of this policy. This is a multi-objective optimisation problem as production due dates cannot be ignored and cost is not directly proportional to electricity consumption anymore. Optimal scheduling even of relatively small production orders is clearly beyond the capability of manual tools or common single objective scheduling optimisation methods. Therefore, a multi-objective scheduling optimisation method has been developed which includes reducing electricity consumption and its related cost as part of the objectives in addition to total weighted tardiness. This research focuses on classical job shop environments which are widely used in the manufacturing industry in China and the rest of the world. A mathematical model for the tri-objectives problem that minimises total electricity cost, total electricity consumption and total weighted tardiness has been developed. A specific heuristic has been devised for investigating how the Rolling Blackout policy affects the performance of existing scheduling plans. This heuristic can also be used as a remedial measurement by plant managers if they do not have access to multi-objective optimisation tools. The Non-dominant Sorting Genetic Algorithm has been used as the basis for solving the optimisation problem. Case studies based on four modified job shop instances have been studied to show the effectiveness of the proposed heuristic and the algorithm.